Deploying data science solutions into production is challenging for both small and large organizations. From platform and tooling wars to architecture and design pattern trade-offs it can get overwhelming for inexperienced teams. Furthermore, many organizations will only go through the painful discovery process once. Adam will share some of his experiences from consulting and leading data teams to successfully deploying machine learning solutions, highlighting some of the more difficult challenges to overcome. You might not be surprised to hear it’s not all down to the tech.
In this episode
Head of Machine Learning Engineering, Origami Energy
Dr Adam Sroka, Head of Machine Learning Engineering at Origami Energy, is an experienced data and AI leader helping organisations unlock value from data by delivering enterprise-scale solutions and building high-performing data and analytics teams from the ground up. Adam shares his thoughts and ideas through public speaking, tech community events, on his blog, and in his podcast. Many organisations aren't getting the most out of their data and many data professionals struggle to communicate their results or the complexity and value of their work in a way that business stakeholders can relate to. Being able to understand both the technology and how it translates to real benefits is key. Simply hiring the most capable people often isn’t enough. The solution is a mix of clear and explicit communication, strong fundamentals and engineering discipline, and an appetite to experiment and iterate to success quickly. If this is something you’re struggling with - either as an organisation finding its feet with data and AI or as a data professional - the approaches and systems Adam has developed over his career will be able to help so please reach out. Cutting-edge data technologies are redefining every industry and adopting these new ways of working can be difficult and frustrating. One day, there will be best practices and playbooks for how to maximise the value from your data and teams, but until then Adam is eager to share his experiences in both business and data and shed some light on what works.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.
Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.